Biology subjects

Andersh, K. M.

Publications and source records attributed to Andersh, K. M..

3 recordsLinked to original sources

bioRxiv2025

Proteomic Analysis of Endemic Viral Infections in Neuronsoffers Insights into Neurodegenerative Diseases

Endemic viral infections with low pathogenicity are often overlooked due to their mild symptoms, yet they can exert long-term effects on cellular function and contribute to disease pathogenesis. While viral infections have been implicated in neurodegenerative disorders, their impact on the neuronal proteome remains poorly understood. Here, we differentiated human induced pluripotent stem cells (KOLF2.1J) into mature neurons to investigate virus-induced proteomic changes following infection with five neurotropic endemic human viruses: Herpes simplex virus 1 (HSV-1), Human coronavirus 229E (HCoV-229E), Epstein-Barr virus (EBV), Varicella-Zoster virus (VZV), and Influenza A virus (H1N1). Given that these viruses can infect adults and have the potential to cross the placental barrier, their molecular impact on neurons may be relevant across the lifespan. Using mass spectrometry-based proteomics with a customized library for simultaneous detection of human and viral proteins, we confirmed successful infections and identified virus-specific proteomic signatures. Notably, virus-induced protein expression changes converged on key neuronal pathways, including those associated with neurodegeneration. Gene co-expression network analysis identified protein modules correlated with viral proteins. Pathway enrichment analysis of these modules revealed associations with the nervous system, including pathways linked to Alzheimers and Parkinsons disease. Remarkably, several viral-induced proteomic alterations overlapped with changes observed in postmortem Alzheimers patient brains, suggesting a mechanistic connection between viral exposure and neurodegenerative disease progression. These findings provide molecular insights into how common viral infections perturb neuronal homeostasis and may contribute to neurodegenerative pathology, highlighting the need to consider endemic viruses as potential environmental risk factors in neurological disorders.

neuroscience↗

bioRxiv2024

CNV-Finder: Streamlining Copy Number Variation Discovery

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods--Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), and alpha-Synuclein (SNCA)--which may be relevant to neurological diseases such as Alzheimers disease (AD), Parkinsons disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinsons Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finders interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

bioinformatics↗

bioRxiv2024

IL1A enhances TNF-induced retinal ganglion cell death

Glaucoma is a neurodegenerative disease that leads to the death of retinal ganglion cells (RGCs). A growing body of literature suggests a role for neuroinflammation in RGC death after glaucoma-relevant insults. For instance, it was shown that deficiency of three proinflammatory cytokines, complement component 1, subcomponent q (C1q), interleukin 1 alpha (Il1a), and tumor necrosis factor (Tnf), resulted in near complete protection of RGCs after two glaucoma-relevant insults, optic nerve injury and ocular hypertension. While TNF and C1Q have been extensively investigated in glaucoma-relevant model systems, the role of IL1A in RGC is not as well defined. Thus, we investigated the direct neurotoxicity of IL1A on RGCs in vivo. Intravitreal injection of IL1A did not result in RGC death at either 14 days or 12 weeks after insult. Consistent with previous studies, TNF injection did not result in significant RGC loss at 14 days but did after 12 weeks. Interestingly, IL1A+TNF resulted in a relatively rapid RGC death, driving significant RGC loss two weeks after injection. JUN activation and SARM1 have been implicated in RGC death in glaucoma and after cytokine insult. Using mice deficient in JUN or SARM1, we show RGC loss after IL1A+TNF insult is JUN-independent and SARM1-dependent. Furthermore, RNA-seq analysis showed that RGC death by SARM1 deficiency does not stop the neuroinflammatory response to IL1A+TNF. These findings indicate that IL1A can potentiate TNF-induced RGC death after combined insult is likely driven by a SARM1-dependent RGC intrinsic signaling pathway.

neuroscience↗